Real-Time Climate Risk Assessment for Supply Chain Resilience: A Data-Driven Nowcasting Framework for Colombian Agriculture
- URL: http://arxiv.org/abs/2608.09846v1
- Date: Mon, 10 Aug 2026 17:05:47 GMT
- Title: Real-Time Climate Risk Assessment for Supply Chain Resilience: A Data-Driven Nowcasting Framework for Colombian Agriculture
- Abstract summary: This paper presents a methodological framework for real-time climate risk assessment using data-driven nowcasting techniques to enhance supply chain resilience in Colombian agricultural contexts.<n>Results from synthetic and historical data experiments indicate that short term precipitation nowcasts can be translated into actionable risk indicators for agricultural supply chains.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a methodological framework for real-time climate risk assessment using data-driven nowcasting techniques to enhance supply chain resilience in Colombian agricultural contexts. Climate variability in Colombia, characterized by irregular rainfall, temperature fluctuations, and recurrent extreme events, has a direct impact on agricultural production and logistics, particularly for time sensitive crops. The proposed approach integrates short term climate forecasting based on historical meteorological observations with supply chain risk modeling to establish a conceptual early warning system architecture. A prototype implementation developed in a controlled computational environment demonstrates the feasibility of the framework using historical meteorological and agricultural time series derived from official statistics and reanalysis products, without reliance on satellite imagery or computer vision components. The methodology addresses the integration of climate nowcasting with supply chain decision making through explicit risk mapping, threshold-based categorization, and stakeholder-oriented risk signals. Results from synthetic and historical data experiments indicate that short term precipitation nowcasts can be translated into actionable risk indicators for agricultural supply chains, supporting anticipatory decisions related to inventory, sourcing, and transport.
Related papers
- Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates [1.1503354666872168]
Accurate short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.<n>NDVI forecasting from satellite observations remains challenging due to sparse and irregular sampling caused by cloud coverage.<n>We propose a probabilistic forecasting framework specifically designed for field-level NDVI prediction under clear-sky acquisition constraints.
arXiv Detail & Related papers (2026-02-04T17:48:52Z) - Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers [0.0]
This study proposes a probabilistic machine learning framework for modeling and predicting the dynamics and severity of precipitation.<n>To account for uncertainty, we employ the conformal prediction method, which enables the generation of calibrated non-parametric prediction intervals.
arXiv Detail & Related papers (2025-10-28T10:05:34Z) - Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling [50.3911487821783]
Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms.<n>Here, we present SynCast, a high-resolution neural forecasting model that integrates meteorological and air composition data to improve predictions of both average and extreme pollution levels.
arXiv Detail & Related papers (2025-10-28T01:18:00Z) - Multi-Hazard Early Warning Systems for Agriculture with Featural-Temporal Explanations [5.363664265121231]
Climate extremes present escalating risks to agriculture.<n>Traditional single-hazard forecasting methods fall short in capturing complex interactions among concurrent climatic events.<n>In this paper, we combine sequential deep learning models and advanced Explainable Artificial Intelligence (XAI) techniques to introduce a multi-hazard forecasting framework for agriculture.
arXiv Detail & Related papers (2025-07-30T05:16:35Z) - Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network [47.699409089023696]
Air quality prediction plays a crucial role in public health and environmental protection.<n>Existing works underestimate the critical role atmospheric conditions in air quality prediction.<n> MDSTNet is an encoder framework explicitly that captures atmosphere-pollution dependencies for prediction.<n>ChinaAirNet is the first dataset combining air quality records with multi-pressure-level meteorological observations.
arXiv Detail & Related papers (2025-04-14T09:18:11Z) - Regional climate risk assessment from climate models using probabilistic machine learning [12.737495484442443]
GenFocal is a general-purpose, end-to-end generative framework for complex climate processes interacting at finetemporal scales.<n>It more accurately assesses extreme risk in the current climate than leading approaches.<n>GenFocal shows compelling results consistent with the literature on projecting climate impact on decadal timescales.
arXiv Detail & Related papers (2024-12-11T03:52:17Z) - Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system.
We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
arXiv Detail & Related papers (2024-11-07T22:29:05Z) - Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
We design a conceptual fine-grained causal model named TBN Granger Causality.
Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner.
We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
arXiv Detail & Related papers (2024-08-08T06:47:21Z) - Climate-Driven Doubling of U.S. Maize Loss Probability: Interactive Simulation with Neural Network Monte Carlo [0.0]
We examine future risk within the U.S. Corn Belt for one such crucial institution: the U.S. Federal Crop Insurance Program.<n>We predict the impacts of climate-driven crop loss at a policy-salient "risk unit" scale.
arXiv Detail & Related papers (2024-08-05T03:38:38Z) - Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests [47.285748922842444]
We train a deep neural network to predict a phenological index from meteorological time series.
We find that this approach outperforms traditional process-based models.
arXiv Detail & Related papers (2024-01-08T15:29:23Z) - Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.